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1. CS-2251 DESIGN AND ANALYSIS OF ALGORITHMS
UNIT-I
1. Define Algorithm.
An algorithm is a sequence of unambiguous instructions for solving a problem in
a finite amount of time.
2. Write about Notion of Algorithm.
Problem
Algorithm
Input
Computer
Output
3. Write a short note on Algorithm Design and Analysis of Process.
 Understand the problem
 Decide on
 Computational Device
 Exact Vs Approximate Algorithms
 Data Structures
 Algorithm Design Techniques
 Design an algorithms
 Prove Correctness
 Analyze the Algorithm
 Code the Algorithm
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4. What are the 2 kinds of Algorithm Efficiency
 Time Efficiency-How fast your algorithm runs?
 Spce Efficiency-How much extra memory your algorithm needs?
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5. How can you specify Algorithms?
Algorithms can be specified in a natural language or pseudo code.
6. What is Pseudo Code?
Pseudo Code is a mixture of Natural Language and Programming Language
Constructs such as functions, loops, decision making statements..etc
7. What are the Important Problem Types?
 Sorting
 Searching
 String Processing
 Graph Problem
 Cominatorial Problem
 Geometric Problem
 Numerical Problem
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2. 8. How can you Classify Algorithms
Among several ways to classify algorithms, the 2 principal alternatives are
 To group algorithms according to types of problem they solve
 To group algorithms according to underlying design techniques they are based
upon
9. Write the Euclid Algorithm
Algorithm Euclid(m,n)
Step 1: While n not equal do
Step 2: r = m mod r
Step 3: m=n
Step 4: n=r
Step 5: return n
10. What is Sorting Problem?
Sorting algorithm is rearrange the items of a given list in descending/ascending
order. Sorting algorithms classified into
 Stable Sorting Algorithm
 Non-Stable Algorithm
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11.What is Searching Problem?
Finding a given value, called search key in a given set. Searching Algorithms
needs more memory space and sorted array.
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12. What is Graph Problem?
Graph is a collection of edges and vertices. G=(V,E). For eg. Traversal
Algorithms, Shortest Path Algorithm, Graph Coloring Problem
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13. What is Combinatorial Problem.
This problem that ask to find a combinatorial object such as permutations,
combinations or a subset. Combinatorial problems are most difficult to solve. For eg
Traveling sales man problem
14. Differentiate Time Efficiency and Space Efficiency?
Time Efficiency measured by counting the number of times the algorithms basic
operation is excuted. Space Efficiency is measured by counting the number of extra
memory units consumed by the algorithm.
15. What are the features of efficient algorithm?
 Free of ambiguity
 Efficient in execution time
 Concise and compact
 Completeness
 Definiteness
 Finiteness
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3. 16. Define Order of Algorithm
The order of algorithm is a standard notation of an algorithm that has been
developed to represent function that bound the computing time for algorithms.
The order of an algorithm is a way of defining its efficiency. It is usually referred
as O-notation
17. Define Big Omega Notation.
Omega notation provides a lower bound for the function t..
A function t(n) is said to in Omega (g(n)), if there exist some positive constant C
and some non negative integer N0, such that
t(n)>=Cg(n) for all n>=n0
18. What is Big ‘Oh’ Notation.
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The Big ‘Oh’ notation provides an upper bound for the function t.
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A function t(n) is said to be in O(g(n)), if there exist some positive constant C and
some non negative number No, such that ,
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t(n)<=Cg(n), for all n>=no
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19. What are the different types of time complexity?
The time complexity can be classified into 3 types, they are
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 Worst case analysis
 Average case analysis
 Best case analysis
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20.How the algorithm’s time efficiency is measured.
Time efficiency indicates how fast an algorithm runs. Time taken by a program
to complete its task depends on the number of steps in an algorithm.
The time taken by an algorithm is the sum of compile time and execution time.
The compile time does not depend on the instance characteristics
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4. UNIT-II
1. What is Empirical Analysis?
It is performed by running a program implementing the algorithm on a sample of
inputs and analyzing the data observed . This involves generating pseudorandom
numbers.
2. Define Convex-Hull Problem.
A set of points(finite or infinite) on the plane is called convex if for any two
points P and Q in the set, the entire line segment with the end points at P and Q belongs
to the set
3. What is Divide and Conquer Algorithm
It is a general algorithm design techniques that solved a problem’s instance by
dividing it into several smaller instance, solving each of them recursively, and then
combining their solutions to the original instance of the problem.
4. What are the Features of Algorithm Visualization.
 Consistent
 Interactive
 Very Clear and Concise
 Emphasize the visual component
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5. Define O-Notation.
A function t(n) is said to be in O (g(n)), denoted t(n) Є O (g(n)), if t(n) is bounded
above by some constant multiple of g(n) for all large n, ie ., if there exist some positive
constant c and some nonnegative integer n0 such that
t(n) <= cg(n) for all n >= n0
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6. What is Algorithm Visualization?
It is defined as the use of images to convey some useful information about
algorithms.
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7. Define Static Algorithm Visualization?
Static Algorithm Visualization shows an algorithms progress through a series of
still images. On other hand, Algorithm animation shows a continuous movie like
presentation of an algorithm’s operation.
8. What is Fibonacci Numbers?
The Fibonacci numbers are an important sequence of integers in which every
element is equal to the sum of its two immediate predecessors. There are several
algorithms for computing the Fibonacci numbers with drastically different efficiency.
9. What are the Classification of Algorithm Visualization?
 Static Algorithm Visualization
 Dynamic Algorithm Vsualization
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5. 10. What is Brute Force?
Brute Force is a straightforward approach to solving problem, usually directly
based on the problem’s statement and definitions of the concepts involved..
11. What are the different criteria used to improve the effectiveness of algorithm?
Input- Zero or more quantities are externally supplied
Output-At least one quantity is produced
Definiteness-Each instruction is clear and unambiguous
Finiteness-If we trace out the instructions of an algorithm, then for all case the
algorithm terminates after a finite number of steps
Effectiveness-Every instruction must be very clear
12. What is recursive call?
An algorithm is said to be recursive if the same algorithm invoked in the body.
There are 2 types of algorithm. They are
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1) Direct Recursive
2) Indirect Recursive
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13. What is meant by Direct Recursive call?
An algorithm that calls itself is direct recursive call.
Eg.
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int fun(int x)
{
if(x<=0)
return x;
return (fun(x-1));
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}
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14. Define indirect recursive call?
Algorithm A is said to be indirect recursive if it calls another algorithm which in
turn call A.
Eg.
int fun(int x)
{
if(x<=0)
return x;
return (fun1(x-1));
}
int fun1(int y)
{
return fun(y-1)
}
15. List the application areas of algorithm visualization?
The 2 application are of algorithm visualization are Research and Education
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6. 16. Give some example for animation systems.
1. BALSA
2. TANGO
3. ZEUS
17. Define Interpolation?
Interpolation is approach, which deals with values with in the sample’s range.
18. Define Extrapolation?
Extrapolation is a approach, which deals with values of n, that are outside of the
range of the samples values.
19. Define profiling?
Profiling is an important resource in the empirical analysis of an algorithms
running time.
Measuring time spent on different segments of a program can pinpoint a
bottleneck in the program’s performance that can be missed by an abstract deliberation
about the algorithm’s basic operations. The process of getting such data is called
profiling.
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20. What is meant by basic operations.?
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A basic operation is the operation that contributes most toward running time .
Typically, it is the most time consuming operation in the algorithm’s innermost loop.
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UNIT III
1. What is articulation point?
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A vertex of a connected graph G is said to be in articulation point, if its
removal with all edges incident to it breaks the graph into disjoint pieces.
2. List the advantages of binary search?




Less time is consumed
The processing speed is fast
The number of iterations is less. It take n/2 iterations.
Binary search, which is used in Fibonacci Series, involves addition and
subtraction rather than division
 It is priori analysis, since it can be analyzed before execution
3. Define binary search Tree?
A binary search tree t is a binary tree, either it is empty or each node in the tree
contains an identifier and
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7. 1) all identifiers in the left subtree of t are less than the identifier in the root node t.
2) all identifier in the right subtree of t are greater than the identifier in the root node t.
3) the left and right subtrees of t are also binary search trees.
4. Explain the principle used in quick sort?
It is a partition method using the particular key the given table is partitioned into 2
subtables so that first, the original key will be its proper position in the sorted sequence
and secondly, all keys to the left of this key will be less in value and all keys to the right
of it will be greater in values
5. What is binary search?
The binary search algorithm is one of the most efficient searching techniques
which requires the list to be sorted in ascending order.
To search for an element in the list, the binary search algorithms split the list and
locate the middle element of the list.
First compare middle key K1, with given key K
If K1=K then the element is found.
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6. What are the objectives of sorting algorithm?
To rearrange the items of a given list
To search an element in the list
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7. Why is bubble sort called by the name?
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The sorting problem is to compare adjacent elements of the list and exchange
them if they are out of order. By doing it repeatedly, we end up bubbling up the largest
element to the last position on the list. The next pass bubbles up the second largest
element and so on until, after n-1 pass the list is sorted.
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8. What are the 3 variations in transform and conquer?
The principle variations of transform and conquer techniques are



Instance Simplification
Representation Change
Problem Reduction
9. Explain principle of Optimality?
The principle of optimality says that an optimal solution to any instance of
an optimization problem is composed of optimal solution to its subinstances.
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8. 10. What is need for finding minimum spanning tree?
Spanning tree has many applications. Any connected graph with n
vertices mush have atleast n-1 edges and all connected graphs with n-1 edges are
trees
If the nodes of G represent cities and edges represent possible
communication links connecting 2 cities, then the minimum number of links
needed to connect the n cities is n-1. There fore, it is necessary for finding
minimum spanning tree.
11. What is critical path?
A path of longest length is called critical path. For example tree
12. What is spanning tree?
Let G={V,E} be an undirected connected graph. A sub graph t={V,E} of G is a
spanning tree of G, if t is a tree.
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13. What is Dynamic programming?
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Dynamic programming is an algorithm design technique for solving
problem with overlapping subprograms. The smaller subprograms are
solved only once and recording the results in a table from which the
solution to the original problem is obtained.
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14. What is greedy method?
The greedy method is the most straight forward design, which is applied
for change making problem.
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The greedy technique suggests constructing a solution to an optimization
problem through a sequence of steps, each expanding a partially
constructed solution obtained so far, until a complete solution to the
problem is reached.
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On each step, the choice made must be feasible, locally optimal and
irrevocable.
15. List the advantage of greedy algorithm
1) Greedy algorithm produces a feasible solution
2) Greedy method is very simple to solve a problem
3) Greedy method provides an optimal solution directly.
16. Define the term control abstraction?
Control abstraction is a procedure whose flow of control is clear but
whose primary operations are specified by other procedures whose precise
meanings are left undefined.
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9. 17. List the applications of minimum spanning tree?
Spanning tree are used to obtain an independent set of circuit equations for
an electric network.
Another application of spanning tree arises from the property that a
spanning tree is a minimal sub graph G’ of G such that
V(G’)=V(G) and G’ is connected
18. Define AVL Tree
An AVL Tree is a binary search tree in which the balance factor of every
node, which the balance factor of every node, which is defined as the difference
between the heights of the node’s left and right sub trees is either 0 or +1 or -1
19. What do you mean by row major and column major?
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In a given matrix, the maximum elements in a particular row is called row
major.
In a given matrix, the maximum elements in a particular column is called
column major.
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20. What is Minimum Cost Spanning Tree?
A minimum cost spanning tree of a weighted connected graph is its
spanning tree of the smallest weight, where the weight of the tree is
defined as the sum of the weights on all its edges.
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UNIT-IV
Define mode?
A mode is a value that occur often in a given list of numbers.
For example, the list is 5,1,5,7,6,5,7,5 .. the mode is 5.
2. Define rotation?
A rotation in an AVL tree is a local transformation of its subtree rooted at
a node, which is performed, when the balance factor of a node becomes either +2
or -2.
If an insertion or deletion of a new node in AVL Tree creates a tree with a
violated balance requirement, then the tree is restructured by performing special
transformation called rotation, that restore the balance required.
3.
What are the different types of rotations?
The four types of rotations are
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10. 1)
2)
3)
4)
Right rotation
Left rotation
Double right-left rotation
Double left right rotation.
4. What are the drawbacks of AVL Tree?
1) Frequent rotations are needed to maintain balances fro the tree’s
nodes.
2) Deletion is difficult due to the frequency rotations.
3) AVL tree is not considered as standard structure for implementing
dictionaries.
5. What is 2-3 tree?
A 2-3 tree is a tree have 2 kinds of nodes. They are 2 nodes and 3 nodes.
A 2 nodes contains single key k and has 2 children
A 3 nodes contains two ordered key K1 and K2(K1<k2) and has three
children.
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6. Define Heap.
Heap is a partially ordered data structure that is especially suitable for
implementing priority queues
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A heap is said to be a max heap, then the children of every node have a
value less than that node.
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A heap is said to be a min heap, then the children of every node have a
value greater than that node.
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7. What is a priority queue?
Priority queue is a data structure in which the intrinsic ordering of the
elements does determine the results of its basic operations
Ascending and descending priority queue are the 2 types of priority queue.
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8. Define warshall’s algorithm?
Warshall’s algorithm is an application of dynamic programming
technique, which is used to find the transitive closure of a directed graph.
9.
Define Floyd’s algorithm?
Floyd’s algorithm is an application, which is used to find all the pairs
shortest paths problem.
Floyd’s algorithm is applicable to both directed and undirected weighted
graph, but they do not contain a cycle of a negative length.
10. Define prim’s algorithm.
Prim’s algorithm is a greedy and efficient algorithm, which is used to find
the minimum spanning tree of a weighted connected graph.
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11. 11. How efficient is prim’s algorithm?
The efficiency of the prim’s algorithm depends on data structure chosen
for the graph.
12. Define Kruskal’s algorithm?
Kruskal’s algorithm is another greedy algorithm for the minimum
spanning tree problem. Kruskal’s algorithm constructs a minimum
spanning tree by selecting edges in increasing order of their weights
provided that the inclusion does not create a cycle.
Kruskals algorithm provides a optimal solution.
13. What is path compression?
The better efficiency can be obtained by combining either variation of
quick union with path compression.
Path compression make every node encountered during the execution of a
find operation point to the tree’s node.
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14. Define Dijkstra’s Algorithm?
Dijkstra’s algorithm solves the single source shortest path problem of
finding shortest paths from a given vertex( the source), to all the other
vertices of a weighted graph or digraph.
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Dijkstra’s algorithm provides a correct solution for a graph with non
negative weights.
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15. Define Huffman trees?
A Huffman tree is binary tree that minimizes the weighted path length
from the root to the leaves containing a set of predefined weights.
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The most important application of Huffman trees are Huffman code.
16. What do you mean by Huffman code?
A Huffman code is a optimal prefix tree variable length encoding scheme
that assigns bit strings to characters based on their frequencies in a given
text.
17. What is meant by compression ratio?
Huffman’s code achieves the compression ratio, which is a standard
measure of compression algorithm’s effectiveness of
(3-2.25)/3*100 = 0.75/3*100
= 0.25 *100
= 25%.
18. List the advantage of Huffman’s encoding?
a. Huffman’s encoding is one of the most important file compression
methods.
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12. b. It is simple
c. It is versatility
d. It provides optimal and minimum length encoding
19. What is dynamic Huffman encoding?
In dynamic Huffman encoding, the coding tree is updated each time a new
character is read from the source text.
Dynamic Huffman encoding is used to overcome the drawback of simplest
version.
20. What do you mean by optimal solution?
Given a problem with n inputs, we obtain a subset that satisfies come
constraints. Any subset that satisfies these constraints is called a feasible
solution.
A feasible solution, which either maximizes or minimizes a given
objective function is called optimal solution.
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UNIT-V
1.
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Define backtracking?
Depth first node generation with bounding function is called backtracking.
The backtracking algorithm has its virtue the ability to yield the answer
with far fewer than m trials.
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2. What is Hamiltonian cycle in an undirected graph?
A Hamiltonian cycle is round trip along n edges of G that visits every
vertex once and returns to its starting position.
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What is Feasible solution?
It is obtained from given n inputs
Subsets that satisfies some constraints are called feasible solution.
It is obtained based on some constraints
4.
What is optimal solution?
It is obtained from feasible solution.
Feasible solution that maximizes or minimizes a given objective function
It is obtained based on objective function.
5. List the application of backtracking technique?
The application of backtracking technique is 8-Queens problem
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13. 6. Given an application for knapsack problem?
The knapsack problem is problem in combinatorial optimization. It
derives its name from the maximum problem of choosing possible
essential that can fit into one bag to be carried on a trip. A similar
problem very often appears in business, combinatory, complexity theory,
cryptography and applied mathematics.
7. Define subset sum problem?
Subset sum problem is a problem, which is used to find a subset of a given
set S={S1,S2,S3,…….Sn} of n positive integers whose sum is equal to
given positive integer d.
8.
What is heuristic?
A heuristic is a common sense rule drawn from experience rather than
from a mathematically proved assertion.
For example, going to the nearest un visited city in the travelling salesman
problem is good example for heuristic.
9.
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State the concept of branch and bound method?
The branch and bound method refers to all state space search methods in
which all children of the E-Node are generated before any other live node
can become the E-node.
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10. Give the upper bound and lower bound of matrix multiplication algorithm?
Upper bound: The given algorithm does n*n*n multiplication hence at
most n*n*n multiplication are necessary.
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Lower bound: It has been proved in the literature that at least n*n
multiplication are necessary.
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11. What is state space tree?
Backtracking and branch bound are based on the construction of a state
space tree, whose nodes reflect specific choices made for a solution’s
component
Its root represents an initial state before the search for a solution begins.
The nodes of the first level in the tree represent the choices made for the
first component of a solution, the nodes of the second level represent the
choices for the second components & so on
12. What is promising and non promising node?
A node in a state space tree is said to be promising, if it corresponds to a
partially constructed solution that may still lead to a complete solution.
Otherwise, a node is called non- promising.
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14. 13. What are the additional items are required for branch and bound compare
to backtracking technique?
Compared to backtracking, branch and bound requires 2 additional items.
1) A way to provide, for every node of a node of a state space tree, a
bound on the best value of the objective function on any solution that
can be obtained by adding further components to the partial solution
represented by the node.
2) The value of the best solution seen so far.
14. Differentiate backtracking and branch bound techniques.
Backtracking is applicable only to non optimization problems.
Backtracking generates state space tree in depth first manner.
Branch and bound is applicable only to optimization problem.
Branch and bound generated a node of state space tree using best first rule.
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15. What is called all pair shortest path problem?
Given a weighted connected graph, the all pairs shortest paths problem is
to find the distances from each vertex to all other vertices.
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16. When do you say a tree as minimum spanning tree?
A spanning tree is said to be minimum spanning tree when the weight of
the spanning tree is smallest, where the weight of a tree is defined as the sum of
the weight of all its edges.s
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17. How will you construct an optimal binary search tree?
A binary search tree is one of the most important data structures in
computer sciences. its principal applications are to implement a dictionary, a set
of elements with the operations of searching, insertion and deletion. If
probabilities of searching for elements of a set are known as optimal binary search
tree, for which the average number of comparisons in a search is the smallest
possible.
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18. What is the runtime of shortest path algorithm?
The runtime of shortest path algorithm is θ((n+|E|) log n)
19. What is mathematical modeling?
Mathematical modeling is a method of expressing a problem in terms of
purely Mathematical objects such as variables, functions and equations.
20. What is pre structure?
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15. Pre structuring is a type of technique that exploits space for time tradeoffs
simply uses extra space of facilities faster and or more flexible access to data.
21. Define Heap sort?
Heap sort is a sorting algorithm. Heap sort is a 2 stage algorithm. The
two stages are
Stage 1- Heap Construction- Construct a heap for a given array of
elements
Stage 2- Maximum Deletion- Apply the root deletion operation n-1
times to the remaining heap.
16 MARKS
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UNIT-I
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1) What are the important problem types focused by the researchers? Explain all the
types with example
2) What is empirical analysis of an algorithm? Discuss its strength and weakness.
3) Discuss the fundamentals of analysis framework.
4) Explain the various asymptotic notations used in algorithm techniques.
5) Describe briefly the notions of complexity of algorithm.
6) What is pseudo code? Explain with an example.
7) Explain various criteria used for analyzing algorithms.
8) Discuss briefly the sequence of steps in designing and analyzing algorithm
9) Explain the general framework for analyzing the efficiency of algorithm
10) Explain the fundamentals of algorithmic problem solving with algorithm design
and analysis process diagram
11) Explain simple factoring algorithm with example
12) Explain the Euclid’s algorithm with example
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UNIT-II
1) Discuss the general plan for analyzing the efficiency of non recursive algorithms
2) Write an algorithm for a given numbers to n generate the nth number of the
Fibonacci sequences.
3) Explain the necessary steps for analyzing the efficiency of recursive algorithms.
4) Write short notes on algorithm visualization
5) Design a recursive algorithm to compute the factorial function F(n) =n! for an
arbitrary non negative integer n also derive the recurrence relation.
6) Explain the general plan for mathematical analysis of recursive algorithms with
example.
7) Explain algorithm visualization with examples.
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16. 8) Write an algorithm to find the number of binary digits in the binary representation
of a positive decimal integer and analyze its efficiency
9) Write an algorithm for finding of the largest element in a list of n numbers
10) Differentiate mathematical analysis of algorithm and empirical analysis of
algorithm
11) Explain static algorithm visualization and dynamic algorithm visualization with
example
12) Explain algorithm animation with example
UNIT-III
1) Explain Brute force method with proper example
2) Explain selection sort and bubble sort algorithm with proper example
3) Explain sequential searching algorithm with example
4) Explain brute force string matching algorithm with example
5) Explain the divide and conquer algorithms with example
6) Explain merge sort algorithm with example
7) Explain quick sort algorithm with example
8) Explain binary search algorithm with example
9) Explain binary tree traversals and related properties with example
10) Explain insertion sort with example
11) Explain Depth First Search and Breadth First Search
12) Write an algorithm to sort a set of N numbers using insertion sort
13) Trace the algorithm for the following set of number 20, 35, 18, 8 14, 41, 3, 39
14) Mention any three search algorithms which is preferred in general? why?
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UNIT-IV
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1) Explain Transform and conquer techniques with example
2) Explain pre sorting
3) Explain balanced search tree with example
4) Explain Binary Search Tree with example
5) Explain AVL Trees with example
6) Explain Warshall’s algorithm with example
7) Explain Floyd’s algorithm with example
8) Explain Optimal Binary search trees with example
9) Explain Prim’s algorithm with example
10) Explain Kruskal’s Algorithm
11) Explain Dijkstra’s Algorithm
12) Explain Huffman Trees
13) Explain the method of finding the minimum spanning tree for connected graph
using prim’;s algorithm
14) How will you find the shortest path between two given vertices using Dijkkstra’s
Algorithm? Explain
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17. UNIT-V
1) Explain backtracking with example
2) Explain n-Queen’s problem with example
3) Explain Hamiltonian circuit problem with proper example
4) Explain subset problem with proper example
5) Explain Branch and bound Techniques with proper example
6) Explain Knapsack problem with example
7) Explain travelling salesman problem with example
8) Explain strassen’s matrix multiplication problem with example
9) Discuss the features of travelling salesman problem
10) Given a graph G={V,E,W} shown in the figure where vertices refer to cities,
edges refer to connection between cities, weight associated with each edge
represents the cost. Find out the minimum cost for the salesman
11) Discuss NP completeness in Knapsack problem with justification
12) Apply backtracking technique to solve the following instance of the subset sum
problem S={1,3,4,5} and d=11
13) Explain subset problem and discuss the possible solution strategies using
backtracking
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